Enhancing Brazilian Sign Language Recognition Through Skeleton Image Representation

Effective communication is paramount for the inclusion of deaf individuals in society. However, persistent communication barriers due to limited Sign Language (SL) knowledge hinder their full participation. In this context, Sign Language Recognition (SLR) systems have been developed to improve commu...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings - Brazilian Symposium on Computer Graphics and Image Processing s. 1 - 6
Hlavní autoři: Alves, Carlos Eduardo G. R., De A. Boldt, Francisco, Paixao, Thiago M.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 30.09.2024
Témata:
Accuracy
Assistive technologies
Convolutional neural networks
Image recognition
Image representation
Measurement
Network architecture
Sign language
Skeleton
Video sequences
ISSN:2377-5416
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Effective communication is paramount for the inclusion of deaf individuals in society. However, persistent communication barriers due to limited Sign Language (SL) knowledge hinder their full participation. In this context, Sign Language Recognition (SLR) systems have been developed to improve communication between signing and non-signing individuals. In particular, there is the problem of recognizing isolated signs (Isolated Sign Language Recognition, ISLR) of great relevance in the development of vision-based SL search engines, learning tools, and translation systems. This work proposes an ISLR approach where body, hands, and facial landmarks are extracted throughout time and encoded as 2-D images. These images are processed by a convolutional neural network, which maps the visual-temporal information into a sign label. Experimental results demonstrate that our method surpassed the state-of-the-art in terms of performance metrics on two widely recognized datasets in Brazilian Sign Language (LIBRAS), the primary focus of this study. In addition to being more accurate, our method is more time-efficient and easier to train due to its reliance on a simpler network architecture and solely RGB data as input. Source code and pre-trained models are publicly available at https://github.com/Dudu197/sign-language-recognition.
ISSN:2377-5416
DOI:10.1109/SIBGRAPI62404.2024.10716301